An example of an application for neuro-fuzzy inference apparatus is in the automation of health management (HM) in a complex system, in particular among a “system of systems” having network enabled capability (NEC). Monitoring such systems effectively is a difficult task, due to the large numbers of sensors, actuators and entities along with the uncertainty of the environment. A monitoring and diagnostic capability integrated with prognostics developments will enable the provision of: 1) real-time support of highly integrated NEC systems; 2) management of uncertainty/change in an NEC environment; 3) expert (human) knowledge for HM; and 4) real-time monitoring information associated with the health of system-of-systems (SoS) to assist human decision-making. Health management focuses on the reliable detection and monitoring of faults and failures of distributed assets. A degree of diagnostic capability already exists at the component level. The main challenge is to bring health management to different levels across a distributed system, as in NEC, thereby enabling major improvements in supportability and reconfiguration.
System diagnosis is an important part of HM systems. Diagnostics provide health information for use in prognostics, reconfiguration and decision-making functions. In recent years, a class of artificial intelligent (AI) technologies has been introduced to help engineers deal with large-scale complex network enabled systems in uncertain environments. Neuro-fuzzy inference (NFI) systems are possibly the best tools available for accounting for qualitative aspects of complexity such as the uncertainty of the environment and are well suited for decision making tasks. NFI systems combine expert knowledge and learning in a hybrid approach. Examples of such techniques applied to industrial plant control applications are described for example in “Knowledge-elicitation and data-mining: Fusing human and industrial plant information” by W. Browne, L. Yao, I. Postlethwaite, S. Lowes, M. Mar, Engineering Applications of Artificial Intelligence 19 (2006) 345-359 and in “Design, implementation and testing of an intelligent knowledge-based system for the supervisory control of a hot rolling mill” by L. Yao, I. Postlethwaite, W. Browne, D. Gu, M. Mar, S. Lowes, Journal of Process Control 15 (2005) 615-628. Both papers are published by Elsevier.
However, in the NEC environment, the associated NFI operations in the fuzzification, the inference and the defuzzification stages increase the quantitative complexity of the problem; the quantitative complexity of problems increases the number of rules in the NFI system, when the number of inputs gets bigger.